Multisensor Input for CPG-Based Sensory—Motor Coordination

International audienceThis paper describes a method for providing in real time a reliable synchronization signal for cyclical motions such as steady-state walking. The approach consists in estimating online a phase variable on the basis of several implicit central pattern generator associated with a set of sensors. These sensors can be of any kind, provided their output strongly reflects the timedmotion of a link. They can be, for example, spatial position or orientation sensors, or foot sole pressure sensors. The principle of the method is to use their outputs as inputs to nonlinear observers of modified Van der Pol oscillators that provide us with several independent estimations of the overall phase of the system. These estimations are then combined within a dynamical filter constituted of a Hopf oscillator. The resulting phase is a reliable indexing of the cyclic behavior of the system, which can finally be used as input to low-level controllers of a robot. Some results illustrate the efficiency of the approach, which can be used to control robots

Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization, and quantitative result

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

Functional Rehabilitation: Coordination of Artificial and Natural Controllers

International audienceWalking and standing abilities, though important for quality of life and participation in social and economic activities, can be adversely affected by central nervous system (CNS) disorders such as spinal cord injury, stroke or traumatic brain injury. One characteristic of motor deficiencies which affect lower extremities is their impact on both static and dynamic postural equilibrium. Depending on the impairment level, functional rehabilitation techniques may be needed for a patient to stand up and walk (Popovic and Sinkjær, 2003). Functional electrical stimulation (FES) can induce contraction of skeletal muscles by applying electrical stimuli to sensory-motor system via electrodes which can be placed on the skin (Kralj et al., 1983), or implanted (Guiraud et al., 2006). FES applications applied to lower limbs include foot drop correction, single joint control, cycling, standing up, walking... (Zhang and Zhu, 2007)..

Gait transition and modulation in a quadruped robot : a brainstem-like modulation approach

In this article, we propose a bio-inspired architecture for a quadruped robot that is able to initiate/stop locomotion; generate different gaits, and to easily select and switch between the different gaits according to the speed and/or the behavioral context. This improves the robot stability and smoothness while locomoting. We apply nonlinear oscillators to model Central Pattern Generators (CPGs). These generate the rhythmic locomotor movements for a quadruped robot. The generated trajectories are modulated by a tonic signal, that encodes the required activity and/or modulation. This drive signal strength is mapped onto sets of CPG parameters. By increasing the drive signal, locomotion can be elicited and velocity increased while switching to the appropriate gaits. This drive signal can be specified according to sensory information or set a priori. The system is implemented in a simulated and real AIBO robot. Results demonstrate the adequacy of the architecture to generate and modulate the required coordinated trajectories according to a velocity increase; and to smoothly and easily switch among the different motor behaviors.The authors gratefully acknowledge Keir Pearson for all the discussions and help. This work is funded by FEDER Funding supported by the Operational Program Competitive Factors COMPETE and National Funding supported by the FCT - Foundation for Science and Technology through project PTDC/EEACRO/100655/2008

Exploiting the inherent coordination of central pattern generator in the Control of humanoid robot walking

Ph.DDOCTOR OF PHILOSOPH

Adaptive quadruped locomotion: learning to detect and avoid an obstacle

Dissertação de mestrado em Engenharia de InformáticaAutonomy and adaptability are key features in the design and construction of a robotic system capable of carrying out tasks in an unstructured and not predefined environment. Such features are generally observed in animals, biological systems that usually serve as an inspiration models to the design of robotic systems. The autonomy and adaptability of these biological systems partially arises from their ability to learn. Animals learn to move and control their own body when young, they learn to survive, to hunt and avoid undesirable situations, from their progenitors. There has been an increasing interest in defining a way to endow these abilities into the design and creation of robotic systems. This dissertation proposes a mechanism that seeks to create a learning module to a quadruped robot controller that enables it to both, detect and avoid an obstacle in its path. The detection is based on a Forward Internal Model (FIM) trained online to create expectations about the robot’s perceptive information. This information is acquired by a set of range sensors that scan the ground in front of the robot in order to detect the obstacle. In order to avoid stepping on the obstacle, the obstacle detections are used to create a map of responses that will change the locomotion according to what is necessary. The map is built and tuned every time the robot fails to step over the obstacle and defines how the robot should act to avoid these situations in the future. Both learning tasks are carried out online and kept active after the robot has learned, enabling the robot to adapt to possible new situations. The proposed architecture was inspired on [14, 17], but applied here to a quadruped robot with different sensors and specific sensor configuration. Also, the mechanism is coupled with the robot’s locomotion generator based in Central Pattern Generators (CPG)s presented in [22]. In order to achieve its goal, the controller send commands to the CPG so that the necessary changes in the locomotion are applied. Results showed the success in both learning tasks. The robot was able to detect the obstacle, and change its locomotion with the acquired information at collision time.Autonomia e capacidade de adaptação são características chave na criação de sistemas robóticos capazes de levar a cabo diversas tarefas em ambientes não especificamente preparados nem configurados para tal. Estas características são comuns nos animais, sistemas biológicos que muitas vezes servem de modelo e inspiração para desenhar e construir sistemas robóticos. A autonomia e adaptabilidade destes sistemas advém parcialmente da sua capacidade de aprender. Quando ainda jovens, os animais aprendem a controlar o seu corpo e a movimentar-se, muitos mamíferos aprendem a caçar e a sobreviver com os seus progenitores. Ultimamente tem havido um crescente interesse em como aplicar estas características no desenho e criação de sistemas robóticos. Nesta dissertação é proposto um mecanismo que permita que um robô quadrúpede seja capaz de detectar e evitar um obstáculo no seu caminho. A detecção é baseada num Forward Internal Model (FIM) que aprende a prever os valores dos sensores de percepção do robô, os quais procuram detectar obstáculos no seu caminho. Por forma a evitar os obstáculos, os sinais de detecção dos obstáculos são usados na criação de um mapa que permitirá ao robô alterar a sua locomoção mediante o que é necessário. Este mapa é construído à medida que o robô falha e tropeça no obstáculo. Nesse momento, o mapa é definido para que tal situação seja evitada no futuro. Ambos os processos de aprendizagem são levados a cabo em tempo de execução e mantêm esse processo activo por forma a possibilitar a readaptação a eventuais novas situações. Este mecanismo foi inspirado nos trabalhos [14, 17], mas aplicados aqui a um quadrúpede com uma configuração de sensores diferente e específica. O mecanismo será interligado ao gerador da locomoção, baseado em Control Pattern Generator (CPG) proposto em [22]. Por forma a atingir o objectivo final, o controlador irá enviar comandos para o CPG a fim da locomoção ser alterada como necessário. Os resultados obtidos mostram o sucesso em ambos os processos de aprendizagem. O robô é capaz de detectar o obstáculo e alterar a sua locomção de acordo com a informação adquirida nos momentos de colisão com o mesmo, conseguindo efectivamente passar por cima do obstáculo sem nenhum tipo de colisão

Parameter identification in networks of dynamical systems

Mathematical models of real systems allow to simulate their behavior in conditions that are not easily or affordably reproducible in real life. Defining accurate models, however, is far from trivial and there is no one-size-fits-all solution. This thesis focuses on parameter identification in models of networks of dynamical systems, considering three case studies that fall under this umbrella: two of them are related to neural networks and one to power grids. The first case study is concerned with central pattern generators, i.e. small neural networks involved in animal locomotion. In this case, a design strategy for optimal tuning of biologically-plausible model parameters is developed, resulting in network models able to reproduce key characteristics of animal locomotion. The second case study is in the context of brain networks. In this case, a method to derive the weights of the connections between brain areas is proposed, utilizing both imaging data and nonlinear dynamics principles. The third and last case study deals with a method for the estimation of the inertia constant, a key parameter in determining the frequency stability in power grids. In this case, the method is customized to different challenging scenarios involving renewable energy sources, resulting in accurate estimations of this parameter